Journal of Intelligent & Fuzzy Systems - Volume 35, issue 6

Authors: Jo, Taeho

Article Type: Research Article

Abstract: This article proposes the modified KNN (K Nearest Neighbor) algorithm which receives a string vector as its input data and is applied to the text summarization. The results from applying the string vector based algorithms to the text categorizations were successful in previous works and the text summarization is able to be viewed into a binary classification where each paragraph is classified into summary or non-summary. In the proposed system, a text which is given as the input is partitioned into a list of paragraphs, each paragraph is classified by the proposed KNN version, and the paragraphs which are classified …into summary are extracted ad the output. The proposed KNN version is empirically validated as the better approach in deciding whether each paragraph is essential or not in news articles and opinions. We need to define and characterize mathematically more operations on string vectors for modifying more advanced machine learning algorithms. Show more

Keywords: String vector, semantic similarity, string vector based KNN, text summarization

DOI: 10.3233/JIFS-169841

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6005-6016, 2018

Authors: Kang, Jiyoung | Lim, Jongkuk | Kim, Changho

Article Type: Research Article

Abstract: In this study, we classified emotional responses of a user by measuring the physiological response signals with the Emotion Collector, which is a wearable band equipped with a multi-sensor device. We collected physiological signal data to measure the delicate hand movements, heart rate, and skin tension of the user analysis with a focus on picking out fear among the user’s emotions. In experiments classifying fear factors into five categories, we were able to identify the characteristics of the user’s physiological responses according to the type of fear. We were also able to find the similarities and differences of physiological and …psychological results for fear. The Emotion Collector is very easy to use, robust, and suitable for mobile and long-time logging of data. It can easily be integrated into other systems or applications. The system is designed for use in emotion research as well as in everyday affective applications such as user-centered service and content. Show more

Keywords: Wearable technology, emotion, fear, multi-sensor, physiological data

DOI: 10.3233/JIFS-169842

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6017-6023, 2018

Authors: Nguyen, Tuan-Linh | Kavuri, Swathi | Lee, Minho

Article Type: Research Article

Abstract: For the artificial intelligence (AI) to effectively mimic humans, understanding humans, more specifically, human emotion is important. Sentiment analysis aims to automatically uncover the underlying sentiment or emotions that humans hold towards an entity. There is high ambiguity of emotion in text data. In this paper, we consider the sentence-level sentiment classification task, and propose a novel type of convolutional neural network combined with fuzzy logic called the Fuzzy Convolutional Neural Network (FCNN) and its associated learning algorithm. The new model is an integration of modified Convolutional Neural Network (CNN) in the fuzzy logic domain. The proposed model benefits from …the use of fuzzy membership degrees to produce more refined outputs, thereby reducing the ambiguities in emotional aspects of sentiment classification. Also it benefits from extracting high-level emotional features due to convolutional neural representation. We compare the performance of our proposed approach with conventional CNN for sentiment classification. The experimental results indicate that the proposed FCNN outperforms the conventional methods for sentiment classification task. Show more

Keywords: Sentiment analysis, fuzzy logic, convolutional neural network, convolutional neuro-fuzzy network

DOI: 10.3233/JIFS-169843

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6025-6034, 2018

Authors: Hyun, Eugin | Jin, Young-Seok

Article Type: Research Article

Abstract: In this paper, we proposed a human-vehicle classification scheme using a Doppler spectrum distribution based on 2D Range-Doppler FMCW (Frequency Modulated Continuous Wave). Typically, because humans have non-rigid motion, multiple reflection points can appear on the Doppler spectrum. However, in the actual field, the Doppler spectrum distribution of a walking human is highly variable over time. Thus method using only this characteristic of the extended Doppler spectrum is limited with regard to human-vehicle classification. In order to improve the target classification performance, we designed two feature. The first is the Doppler spectrum extension features, which is expressed as the number …of Doppler reflection points with magnitudes exceeding reference threshold. Next, we defined the Doppler spectrum variance feature, which is extracted as the difference the reflection points between two successive frames. We can determine how the Doppler spectrum expands with the first feature, and how the Doppler spectra change based on the second feature. To verify the proposed target classification scheme, we measured real data using a 24 GHz FMCW transceiver on an actual road with various scenarios of walking humans and moving vehicles. From an analysis of the results, we confirmed that the thresholds effectively classify humans and vehicles based on the two proposed features. Finally, we verified that the results of the proposed classification scheme using the two features were much better than those using the first feature alone. Show more

Keywords: Automotive radar, feature extraction, pedestrian classification, radar recognition, FMCW radar

DOI: 10.3233/JIFS-169844

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6035-6045, 2018

Authors: Ahn, Hyunsik

Article Type: Research Article

Abstract: For interactions with humans, robots need the capability of describing the events they encountered for verbal communication. In this paper, a sentential cognitive system (SCS) using sentences as a media to represent cognized events for conversational human–robot interaction (HRI) is presented. The SCS comprising multiple modules such as perception and behavior, reasoning, and memory interprets modular events to sentential form from acquired cognitive information and a cognitive grammar linking it to sentences and vice versa. The sentences are stored in a sentential memory for being retrieved by reasoning procedures with the auxiliary memories, an object descriptor and a motion descriptor, …afterward for conversational HRI. In the experiment, the proposed SCS is implemented in a robot and tested with scenarios of spatiotemporal conversation about the handling of objects. The result shows the feasibility and efficiency of the proposed SCS for conversational HRI. Show more

Keywords: Intelligent robot, cognitive system, spatiotemporal reasoning, conversational robot, human–robot interaction

DOI: 10.3233/JIFS-169845

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6047-6059, 2018

Authors: Lee, DongSeop | Kim, MyoungHee | Na, IlKang

Article Type: Research Article

Abstract: The job/career matching has been ever increasingly important social issue in every country. The career matching is to assign the right candidate to the right company. The matching is passive process by employment counselor who cannot go through all profiles of candidates and also companies’ profiles. And there can be a human mistake assigning the wrong candidate to a wrong company. To minimize miss-matching cases, the paper investigates several statistical analysis methods and also proposes an artificial intelligence based method. In this paper, the method named Artificial Intelligence based Design platform (AID) has been developed for career matching between university …students and companies. The efficiency of AID has been proven by comparing the results (matching rate representing perfect matching) obtained by the proposed method AID and statistical methods such as least squares, Pearson correlation, Manhattan distance. The paper shows that AID can produce zero miss-matching for student’s skill and company’s needs while the statistical methods produce more than 30% miss-matching. In other words, AID can assign the right student to the right company. Show more

Keywords: Artificial intelligence, job/career matching, decision support, optimization

DOI: 10.3233/JIFS-169846

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6061-6070, 2018

Authors: Lee, Min-Hyuck | Yeom, Seokwon

Article Type: Research Article

Abstract: Recently, drone technology has developed rapidly for various purposes. A drone is very useful for aerial surveillance due to its remote sensing capability. Multiple target detection and tracking are essential to recognize any harmful threats in advance, however the image captured at a distance is easily degraded due to blurring and noise as well as low resolution. This paper addresses the detection and tracking of moving vehicles with drone imaging. A drone captures video sequences of multiple moving vehicles from a distance. Cars and buses are the objects of interests driving on urban roads. The detection step consists of frame …difference followed by thresholding and morphological operation considering the size of region of interest (ROI). The centroids of the ROI’s are considered measurements for tracking. Tracking is performed with interacting multiple model (IMM) filtering, which estimate the state of vectors and covariance matrices using multiple modes of Kalman filtering. The measurements in the validation region are associated with established tracks by the nearest neighbor rule. In the experiment, total seven moving cars and buses are captured at a long distance by a drone. It will be shown that the proposed method well detects the moving vehicles and achieves a good accuracy in estimating their locations. Show more

Keywords: Drone imaging, object detection, multiple target tracking, IMM filtering, frame difference

DOI: 10.3233/JIFS-169847

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6071-6078, 2018

Authors: Park, Young-Hoon

Article Type: Research Article

Abstract: With the development of intelligent vehicles, in-vehicle security and privacy receive considerable attention from researchers and drivers. The data stored in a vehicle control unit (VCU) may contain privacy-sensitive content, which may be exposed to unauthorized devices or attackers due to the structural problems of the in-vehicle network. To overcome this problem, data can be encrypted with the target module’s key before being transmitted to the in-vehicle network. However, this may lead to another efficiency problem. In this study, we propose a model that adopts the proxy re-encryption (PRE) scheme to the in-vehicle network. This is called privacy-enhancing in-vehicle …network key management (PINK). In the proposed model, the data stored in the VCU is encrypted with a master key. In addition, when the stored data is requested by a node, it is transformed into a ciphertext that can only be decrypted with the node’s key using the PRE scheme. Moreover, for the case in which a special group of nodes request the data several times, simultaneously, a group-based key management scheme i provided. Further it is demonstrated using simulations that the proposed PINK scheme can be efficiently applied to the in-vehicle network. Show more

Keywords: In-vehicle network, proxy re-encryption, privacy, key management

DOI: 10.3233/JIFS-169848

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6079-6087, 2018

Authors: Nguyen, Trong-Kha | Ly, Vu Duc | Hwang, Seong Oun

Article Type: Research Article

Abstract: Recent research in the computer security sector has primarily been qualitative, focusing on detection aspects where malware has already infected the target. Challenges for security vendors include developing better techniques for early detection of malware attacks before they can do malicious damage. Malware prediction models are needed that can describe and predict malware generation processes. In this study, we address the feasibility of quantitative characterization of malware in security assessment, we propose a model with aim to explain the mechanism behind malware generation which contributes to estimating malware discovery. Although several malware modeling systems have been proposed, such models have …shortcomings in related to historical data and do not consider malware data as a time series. Using time series analysis, we provide predictive neural network models for five datasets from Symantec and Malwr. The models explore the structures of malware data along with leveraging non-linear and linear properties to predict the number of future malware. Our examination also reveals that it is possible to model the malware discovery process using a neural network based non-linear model. In addition, our analysis provides insights into understanding the mechanisms that generate malware data series. This information can be useful for intelligence services and vital to threat assessment. Show more

Keywords: Time series forecasting, malware, neural network

DOI: 10.3233/JIFS-169849

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6089-6100, 2018

Authors: Maisen, Chakkraphop | Auephanwiriyakul, Sansanee | Theera-Umpon, Nipon

Article Type: Research Article

Abstract: The Mammographic image is a tool for observing breast cancer. Analyzing difficulties include shape, size variety, nearby tissue, and noise. In this paper, we propose a method to classify mammogram abnormalities based on learning vector quantization inference classifier (LVQIC) with fuzzy co-occurrence matrix (FCOM) textural features. The system is implemented on the Mini-MIAS data set with a 5-class problem, i.e., the classification of architectural distortion (AD), spiculated mass (SPIC), calcification (CALC), well-defined/circumscribed masses (CIRC), and normal (NORM). The implementation is also on a 2-class problem consisting of AD-vs-All, SPIC-vs-All, CALC-vs-All, CIRC-vs-All, and NORM/abnormal. The best blind test result is from …the 5-class problem with features from fuzzy co-occurrence matrix (FCOM) with 4 clusters, co-occurrence distance d  = 2, and 16 prototypes per class. The best classification result is 100% correct classification with 0.03, 0.04, 0.06, and 0.02 false positive rate for AD, SPIC, CALC, and CIRC, respectively. Show more

Keywords: Mammograms, breast cancer, breast abnormality detection, neuro fuzzy, feature selection

DOI: 10.3233/JIFS-169850

Citation: Journal of Intelligent & Fuzzy Systems, vol. 35, no. 6, pp. 6101-6116, 2018